Canadian dairy farmer views about animal welfare
Bibliographic record
Abstract
Concerns regarding the welfare of farm animals continue to grow. Traditionally, research efforts have largely focused on refining existing management practices to improve welfare. However, the incorporation of views from those directly involved in animal care is equally, if not more, important. This study investigated the perspectives of Canadian dairy farmers on animal welfare. We conducted 16 interviews with a total of 22 participants from four provinces across Canada. Recorded audio files and field notes were transcribed, anonymised, and coded using deductive and inductive thematic analysis. The interview data revealed two major themes: (1) animal dimension of animal welfare, including views related to biological functioning, naturalness and affective states; and (2) dairy farmer identity, including, the voice of the 'city', what it means to be a good 'cow-man', and the nature of human-animal relationships. Dairy farmers emphasised biological functioning, but they made numerous references to the emotional and natural living aspects of their animals' lives. Our work also provides evidence that farmers believed it was their duty to care for their animals beyond simply milking cows and making a profit. In terms of the larger debate, this study identified potential shared values with members of the public: opportunities for natural living and agency, attentiveness to individual animals, and the value of life over death. Finally, the emotional relationship that farmers developed with their animals highlights the values dairy farmers have for their animals beyond simply utilitarian function. Overall, these shared values could contribute to constructive dialogue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".